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Tetsuo Sakata

dblp:09/3947 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing
music information retrieval		 0.012000
A practical query-by-humming system for a large music database · ACM Multimedia 2000
Audio and music processing › music information retrieval › melody retrieval
query-by-humming		 0.012000
A practical query-by-humming system for a large music database · ACM Multimedia 2000
Information retrieval
similarity search		 0.012000
A practical query-by-humming system for a large music database · ACM Multimedia 2000

Methods — techniques the papers use, named apart from their topics

similarity retrieval · 0.1ranked retrieval · 0.1
YearPublicationVenuePosition
2000 A practical query-by-humming system for a large music database
abstract
A music retrieval system that accepts hummed tunes as queries is described in this paper. This system uses similarity retrieval because a hummed tune may contain errors. The retrieval result is a list of song names ranked according to the closeness of the match. Our ultimate goal is that the correct song should be first on the list. This means that eventually our system's similarity retrieval should allow for only one correct answer.
Naoko Kosugi, Yuichi Nishihara, Tetsuo Sakata, Masashi Yamamuro, Kazuhiko Kushima
 ACM Multimedia3